315 research outputs found
Equality of Voice: Towards Fair Representation in Crowdsourced Top-K Recommendations
To help their users to discover important items at a particular time, major
websites like Twitter, Yelp, TripAdvisor or NYTimes provide Top-K
recommendations (e.g., 10 Trending Topics, Top 5 Hotels in Paris or 10 Most
Viewed News Stories), which rely on crowdsourced popularity signals to select
the items. However, different sections of a crowd may have different
preferences, and there is a large silent majority who do not explicitly express
their opinion. Also, the crowd often consists of actors like bots, spammers, or
people running orchestrated campaigns. Recommendation algorithms today largely
do not consider such nuances, hence are vulnerable to strategic manipulation by
small but hyper-active user groups.
To fairly aggregate the preferences of all users while recommending top-K
items, we borrow ideas from prior research on social choice theory, and
identify a voting mechanism called Single Transferable Vote (STV) as having
many of the fairness properties we desire in top-K item (s)elections. We
develop an innovative mechanism to attribute preferences of silent majority
which also make STV completely operational. We show the generalizability of our
approach by implementing it on two different real-world datasets. Through
extensive experimentation and comparison with state-of-the-art techniques, we
show that our proposed approach provides maximum user satisfaction, and cuts
down drastically on items disliked by most but hyper-actively promoted by a few
users.Comment: In the proceedings of the Conference on Fairness, Accountability, and
Transparency (FAT* '19). Please cite the conference versio
Swarmalators under competitive time-varying phase interactions
Swarmalators are entities with the simultaneous presence of swarming and
synchronization that reveal emergent collective behavior due to the fascinating
bidirectional interplay between phase and spatial dynamics. Although different
coupling topologies have already been considered, here we introduce
time-varying competitive phase interaction among swarmalators where the
underlying connectivity for attractive and repulsive coupling varies depending
on the vision (sensing) radius. Apart from investigating some fundamental
properties like conservation of center of position and collision avoidance, we
also scrutinize the cases of extreme limits of vision radius. The concurrence
of attractive-repulsive competitive phase coupling allows the exploration of
diverse asymptotic states, like static , and mixed phase wave states, and
we explore the feasible routes of those states through a detailed numerical
analysis. In sole presence of attractive local coupling, we reveal the
occurrence of static cluster synchronization where the number of clusters
depends crucially on the initial distribution of positions and phases of each
swarmalator. In addition, we analytically calculate the sufficient condition
for the emergence of the static synchronization state. We further report the
appearance of the static ring phase wave state and evaluate its radius
theoretically. Finally, we validate our findings using Stuart-Landau
oscillators to describe the phase dynamics of swarmalators subject to
attractive local coupling.Comment: 21 pages, 12 figures; accepted for publication in New Journal of
Physic
Incremental Fairness in Two-Sided Market Platforms: On Smoothly Updating Recommendations
Major online platforms today can be thought of as two-sided markets with
producers and customers of goods and services. There have been concerns that
over-emphasis on customer satisfaction by the platforms may affect the
well-being of the producers. To counter such issues, few recent works have
attempted to incorporate fairness for the producers. However, these studies
have overlooked an important issue in such platforms -- to supposedly improve
customer utility, the underlying algorithms are frequently updated, causing
abrupt changes in the exposure of producers. In this work, we focus on the
fairness issues arising out of such frequent updates, and argue for incremental
updates of the platform algorithms so that the producers have enough time to
adjust (both logistically and mentally) to the change. However, naive
incremental updates may become unfair to the customers. Thus focusing on
recommendations deployed on two-sided platforms, we formulate an ILP based
online optimization to deploy changes incrementally in n steps, where we can
ensure smooth transition of the exposure of items while guaranteeing a minimum
utility for every customer. Evaluations over multiple real world datasets show
that our proposed mechanism for platform updates can be efficient and fair to
both the producers and the customers in two-sided platforms.Comment: To Appear In the Proceedings of 34th AAAI Conference on Artificial
Intelligence (AAAI), New York, USA, Feb 202
ANTIULCER ACTIVITY OF AMARANTHUS SPINOSUS LEAF EXTRACT AND ITS COMPARISION WITH FAMOTIDINE IN SHAY RATS
Anti-ulcer activities of petroleum ether, chloroform, Ethanolic extract of Amaranthus spinosus leaf were tested for anti-gastric ulcer activity in Shay rat model. Oral dose of 400mg/kg of ethanolic extract reduced the ulceration and with 800mg/kg body weight ethanolic extract there is the complete absence of ulceration. The anti-gastric activity of ethanolic extract of A.spinosus 800mg/kg body weight was found to be equal to the effect produced by 2mg/kg of Famotidine orally. The reduction in gastric activity was more with Famotidine and the reduction in peptic activity is more with ethanolic extract of A.spinosus. All extracts are safe up to 4000mg/kg. The result of petroleum ether, chloroform and aqueous extract in respect of anti-ulcer activity are less prominent. Keywords: Amaranthus spinosus, Shay rat model, Famotidine, Peptic activityÂ
Equality of Voice: Towards Fair Representation in Crowdsourced Top-K Recommendations
International audienceTo help their users to discover important items at a particular time, major websites like Twitter, Yelp, TripAdvisor or NYTimes provide Top-K recommendations (e.g., 10 Trending Topics, Top 5 Hotels in Paris or 10 Most Viewed News Stories), which rely on crowd-sourced popularity signals to select the items. However, diferent sections of a crowd may have diferent preferences, and there is a large silent majority who do not explicitly express their opinion. Also, the crowd often consists of actors like bots, spammers, or people running orchestrated campaigns. Recommendation algorithms today largely do not consider such nuances, hence are vulnerable to strategic manipulation by small but hyper-active user groups. To fairly aggregate the preferences of all users while recommending top-K items, we borrow ideas from prior research on social choice theory, and identify a voting mechanism called Single Trans-ferable Vote (STV) as having many of the fairness properties we desire in top-K item (s)elections. We develop an innovative mechanism to attribute preferences of silent majority which also make STV completely operational. We show the generalizability of our approach by implementing it on two diferent real-world datasets. Through extensive experimentation and comparison with state-of-the-art techniques, we show that our proposed approach provides maximum user satisfaction, and cuts down drastically on items disliked by most but hyper-actively promoted by a few users
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